Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
---
title: "Gráficos CO2/GHG/Pop"
author: " "
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
source_code: embed
theme:
version: 4
bootswatch: spacelab
---
```{r setup, include=FALSE}
#Bibliotecas necessárias
library(shiny)
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(plotly)
library(readr)
library(gganimate)
library(tidyverse)
library(lubridate)
library(ggthemes)
library(ggrepel)
#//////////////////////// Chama os arquivos para a montagem dos graficos //////////////////////////////////////
CO2_per_gdp <- read_csv("CO2_per_gdp.csv")
CO2_per_gdp_ppp <- read_csv("CO2_per_gdp_ppp.csv")
GHG_per_gdp <- read_csv("GHG_per_gdp.csv")
GHG_per_gdp_ppp <- read_csv("GHG_per_gdp_ppp.csv")
CO2_per_pop <- read_csv("CO2_per_pop.csv")
GDP_per_POP <- read_csv(("GDP_per_POP.csv"),
locale = locale(decimal_mark = ","))
GHG_per_POP <- read_csv("GHG_per_POP.csv")
dispersao1 <- read_csv("dispersao1.csv")
dispersao2 <- read_csv("dispersao2.csv")
#//////////////////////// Troca o nome das colunas conforme necessario //////////////////////////////////////
CO2_per_gdp_ppp <- rename(CO2_per_gdp_ppp, "Year" = Ano)
GHG_per_gdp <- rename(GHG_per_gdp, "country" = contry_name)
GHG_per_gdp_ppp <- rename(GHG_per_gdp_ppp, "Year" = ano)
CO2_per_pop <- rename(CO2_per_pop, "country" = "contry_name")
CO2_per_pop <- rename(CO2_per_pop, "CO2_kg_per_pop" = "CO2_per_pop")
```
# CO2
row {data-width=650} {.tabset}
----------------------------------------------------
### .CO2 Constant GDP {data-padding=20}
```{r}
# Criação do gráfico CO2_per_gdp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly
grafico_1 = ggplot(data = CO2_per_gdp, aes(x = year, y = CO2_kg_per_US, group = " " ,color = Country)) +
geom_point()+
geom_line( aes(x = year, y = CO2_kg_per_US, color = Country))+
# Tema do fundo do grafico
theme_light()+
# determina a escala dos eixos (x e y)
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0,1.5,0.25))+
# Titulo do grafico e nome dos eixos
labs(title = "CO2 Emissions x GDP - Cumulative (1990 - 2018)", x = " ",
y = " CO2 kg per US$")
# Transforma em interativo
ggplotly(grafico_1)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
### .CO2 GDP (PPP) {data-padding=20}
```{r}
# Faz a criação do gráfico CO2_per_gdp_ppp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly
grafico_2 = ggplot(data = CO2_per_gdp_ppp, aes(x = Year, y = CO2_kg_per_ppp_US, group = " " ,color = Country)) +
geom_point()+
geom_line( aes(x = Year, y = CO2_kg_per_ppp_US, color = Country))+
# Tema do fundo do grafico
theme_light()+
# determina a escala dos eixos (x e y)
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0,2,0.25))+
labs(title = "CO2 Emissions x GDP (PPP) - Cumulative (1990 - 2018)",
x = " ",
y = "CO2 kg per (ppp) US$")
# Transforma em interativo
ggplotly(grafico_2)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
# GHG
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .GHG Constant GDP {data-padding=20}
```{r}
# Faz a criação do gráfico GHG_per_gdp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly
grafico_3 = ggplot(data = GHG_per_gdp,aes(x = year, y = GHG_per_GDP_US, group = " " ,color = country)) +
geom_point()+
geom_line( aes(x = year, y = GHG_per_GDP_US, color = country))+
# Tema do fundo do grafico
theme_light()+
# determina a escala dos eixos (x e y)
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0,2.5,0.25))+
labs(title = "Greenhouse gas (GHG) emissions x Constant GDP - Cumulative(1990 - 2018)",
x = " ",
y = "kg of equivalent CO2 emission per US$")
# Transforma em interativo
ggplotly(grafico_3)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
### .GHG GDP (PPP) {data-padding=20}
```{r}
# Faz a criação do gráfico GHG_per_gdp_ppp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly
grafico_4 = ggplot(data = GHG_per_gdp_ppp, aes(x = Year, y = GHG_per_gdp_ppp_US, group = " " ,color = Country)) +
geom_point()+
geom_line( aes(x = Year, y = GHG_per_gdp_ppp_US, color = Country))+
# Tema do fundo do grafico
theme_light()+
# determina a escala dos eixos (x e y)
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0, 2.5, 0.25))+
labs(title = "Greenhouse gas (GHG) emissions x GDP PPP - Cumulative(1990-2018)",
x = " ",
y = "[kg of equivalent CO2 emission per (ppp) US$]")
# Transforma em interativo
ggplotly(grafico_4)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
# CO2/GHG per Population
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .CO2 per POP {data-padding=20}
```{r}
# grafico CO2 pela população (a cada 1000 Habitantes)
grafico_5 = ggplot(data = CO2_per_pop, aes(x = year, y = CO2_kg_per_pop, group = " " ,color = country)) +
geom_point()+
geom_line( aes(x = year, y = CO2_kg_per_pop, color = country))+
# Tema do fundo
theme_light()+
# Define as escala dos eixos
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0,20,5))+
labs(title = "CO2 Emissions x Population (1990-2018)",
x = " ",
y = "CO2 kg per 1000 hab")
# Transforma o grafico em interativo
ggplotly(grafico_5)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
### .GHG per POP{data-padding=20}
```{r}
# grafico CO2 pela população (a cada 1000 Habitantes)
grafico_5 = ggplot(data = GHG_per_POP, aes(x = year, y = GHG_per_POP, group = " " ,color = country)) +
geom_point()+
geom_line( aes(x = year, y = GHG_per_POP, color = country))+
# Tema do fundo
theme_light()+
# Define as escala dos eixos
scale_x_continuous(breaks = seq(1990,2018))+
scale_y_continuous(breaks = seq(0,35,5))+
labs(title = "Greenhouse gas (GHG) emissions x Population (1990-2018)",
x = " ",
y = "GHG kg per 1000 hab")
# Transforma o grafico em interativo
ggplotly(grafico_5)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
# Dispersão
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .(CO2 / GDP) per (CO2 / pop) {data-padding=20}
```{r}
dispersao1 <- dispersao1 %>%
filter(dispersao1$year %in% "2018")
grafico_7 = ggplot(data = dispersao1, aes(x = CO2_per_pop , y = CO2_per_GDP, color = country)) +
geom_text(label = dispersao1$country)+
scale_x_continuous(breaks = seq(0, 25, 2.5))+
scale_y_continuous(breaks = seq(0, 2, 0.25))+
theme_bw()+
labs(title = "CO2 emissions - per population x per GDP(Year: 2018)",
x = "CO2 per POP (kg of CO2 emissions per 1000 hab)",
y = "CO2 per GDP (kg of CO2 emissions per US$)")
ggplotly(grafico_7)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)
### .(GHG / GDP) per (GHG / pop) {data-padding=20}
```{r}
grafico_8 = ggplot(data = dispersao2, aes(x = GHG_per_POP , y = GHG_per_GDP_US, color = country)) +
geom_text(label = dispersao2$country)+
scale_x_continuous(breaks = seq(0, 25, 5))+
scale_y_continuous(breaks = seq(0, 2, 0.25))+
theme_bw()+
labs(title = "Greenhouse gas emissions - per population x per GDP (Year: 2018)",
x = "GHG per POP (kg of equivalent CO2 emissions per 1000 hab) ",
y = " GHG per GDP (kg of equivalent CO2 emissions per US$)")
ggplotly(grafico_8)
```
> Source: World Bank (2021) / Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)